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Full-Text Articles in Life Sciences
Sources Of Peanut Digging Losses And Strategies To Reduce Losses During Inversion, Loren Samenko
Sources Of Peanut Digging Losses And Strategies To Reduce Losses During Inversion, Loren Samenko
All Theses
Presented research was conducted at Clemson University’s Edisto Research and Education Center to quantify harvest related losses associated with the effects of peanut digger blade geometry, the effects of the peanut digger inversion assembly, and the effects of vine load on digging and strategies to address vine load. Three studies were performed to determine the potential losses incurred during the digging processes; various harvest metrics were analyzed to quantify the effects of the treatments. Five objectives guided the presented research. Objectives of the effects of peanut digger blade geometry study investigated the impact of blade geometry and blade aggression on …
Using Uav Technology Paired With Multispectral Cameras To Assess Crown Rust Epidemics In Oats, Turner A. Graham
Using Uav Technology Paired With Multispectral Cameras To Assess Crown Rust Epidemics In Oats, Turner A. Graham
LSU Master's Theses
Crown rust, caused by Puccinia cornonata f. sp. avenae, is a common disease of oats (Avena sativa) found virtually everywhere oats are cultivated. This disease has caused yield losses of 10 to 40% worldwide. Early detection is important for effective management. A more recently utilized technology in agriculture is unmanned aerial vehicles (UAVs). UAVs, or drones, equipped with cameras are now being used as a resource to take images of fields to identify pests and other issues that may be occurring. Normalized differentiated vegetative index (NDVI) is a numerical indicator used to determine the vegetative health of …
Mississippi Sky Conditions, Joby Czarnecki, Sathishkumar Samiappan, Louis Wasson, C. Daniel Mccraine
Mississippi Sky Conditions, Joby Czarnecki, Sathishkumar Samiappan, Louis Wasson, C. Daniel Mccraine
College of Agriculture & Life Sciences Publications and Scholarship
This dataset consists of approximately 13,000 jpg format images. These images were collected using consumer grade trail cameras manufactured by Browning Trail Cameras. Cameras were installed across Mississippi (USA) in 2019 and 2020 from March through September. Images collected are exclusively oblique, unobstructed views of the sky. Cameras were placed in time-lapse mode and set to collect one image every hour. Our intent in this work was to first compare deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. Sky conditions, and specifically shadowing from clouds, are critical determinants …